Finding the optimal solution to NP-hard problems requires at least exponential time. Thus, heuristic methods are usually applied to obtain acceptable solutions to this kind of problems. In this paper we propose a new ...
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ISBN:
(纸本)9783540735533
Finding the optimal solution to NP-hard problems requires at least exponential time. Thus, heuristic methods are usually applied to obtain acceptable solutions to this kind of problems. In this paper we propose a new type of heuristic algorithms to solve this kind of complex problems. Our algorithm is based on river formation dynamics and provides some advantages over other heuristic methods, like ant colony optimization methods. We present our basic scheme and we illustrate its usefulness applying it to a concrete example: The Traveling Salesman Problem.
Porous carbons as solid adsorbent materials possess effective porosity characteristics that are the most important factors for gas storage. The chemical activating routes facilitate hydrogen storage by adsorbing on th...
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Porous carbons as solid adsorbent materials possess effective porosity characteristics that are the most important factors for gas storage. The chemical activating routes facilitate hydrogen storage by adsorbing on the high surface area and microporous features of porous carbon-based adsorbents. The present research proposed to predict H-2 storage using four nature-inspired algorithms applied in the random forest (RF) model. Various carbon-based adsorbents, chemical activating agents, ratios, micro-structural features, and operational parameters as input variables are applied in the ML model to predict H-2 uptake (wt%). Particle swarm and gray wolf optimizations (PSO and GWO) in the RF model display accuracy in the train and test phases, with an R-2 of similar to 0.98 and 0.91, respectively. Sensitivity analysis demonstrated the ranks for temperature, total pore volume, specific surface area, and micropore volume in first to fourth, with relevancy scores of 1 and 0.48. The feasibility of algorithms in training sizes 80 to 60% evaluated that RMSE and MAE achieved 0.6 to 1, and 0.38 to 0.52. This study contributes to the development of sustainable energy sources by providing a predictive model and insights into the design of porous carbon adsorbents for hydrogen storage. The use of nature-inspired algorithms in the model development process is also a novel approach that could be applied to other areas of materials science and engineering.
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